In order to improve the precision and convergence speed of spatial attitude recognition of product parts,a method of spatial attitude recognition based on structured light 3D vision is proposed.Firstly,image information of product parts is obtained by projector and camera,depth information is obtained by phase shift method,and 3D point cloud data is obtained by point cloud reconstruction of depth map.Then,when the 3D Point cloud data is processed and classified,the Point Network (PointNet) model is established.Finally,an improved Iterative Closest Point (ICP) algorithm is used to register 3D point cloud data,so as to realize the identification of product part attitude.The experiment results show that the accuracy of the method can reach about 96% and the recall rate can be stable at about 92%.In terms of registration accuracy and convergence speed,it is superior to the other two methods.The effectiveness and feasibility of this method are further verified.